One-stage instance segmentation methods remove the bounding box detection and feature re-pooling steps.
AdaptIS [1]} produces masks for objects located on point proposals.
PolarMask [2]} models instance masks in polar coordinates by instance center classification and dense distance regression.
YOLOACT [3]} introduces prototype masks with per-instance coefficients.
SOLO [4]} applies the “instance categories” concept to directly output instance masks based on the location and size.
Grouping-based approaches [5]}, [6]}, [7]}, [8]}, [9]}, [10]} regard segmentation as a bottom-up grouping task by first producing pixel-wise predictions followed by grouping object instances in the post-processing stage.
These one-stage methods, with simpler procedures than their two-stage counterparts, are more efficient but tend to be less accurate.
